(1) Field of the Invention
The invention relates generally to the field of systems and methods for determining models describing motion of vehicles and environmental and sensor characteristics in the presence of model uncertainty and noise. In a preferred embodiment, the invention facilitates selection of the physical processes relating to motion of a signal source moving in a medium, the propagation of the signal through the medium and the interception of the signal by the sensors being in response to received signals which may be corrupted by noise.
(2) Description of the Prior Art
Expert systems can be used to identify likely models of physical phenomena in response to information about the state of the phenomena, particularly where the information is corrupted by noise.
J. Baylog, et al., "Underwater Tracking In The Presence of Modeling Uncertainty," Proc. 21st Asilomar Conference On Signals, Systems And Computers, November 1987, (hereinafter "Baylog, et al.") (incorporated by reference) and D. Ferkinhoff, et al., "Feature Extraction And Interpretation For Dynamic System Model Resolution," Proc. 24th Asilomar Conference On Circuits, Systems And Computers, November 1990, (hereinafter "Ferkinhoff, et al., I") (incorporated by reference) generally describe a system which is used to model the motion of an object through a fluid in response to information which is received by arrays of acoustic sensors placed in the fluid. The acoustic sensors receive acoustic signals which arise from motion of the object through the fluid, or which may be emitted by the object as it propels itself through the fluid. The particular types of sensors in the array may be selected to detect certain types of information. For example, sensors may be selected to provide the azimuth bearing of the object relative to the sensor, the angle of depression or elevation of the object relative to the location of the sensor, and the frequency of the signal. Information from these sensors may be used to provide evidence to support models representing the motion of the object through the fluid.
One problem that arises in determining the likelihood that particular models accurately represent physical processes, including the motion of the target object, is that the sensors are likely to receive not only the signals as generated by the moving physical process or object, but also noise. Indeed, noise may be due not only to acoustic signals generated by other objects than the one being modeled, but also by reflections of the signals being generated by the object being modeled off of discontinuities in the fluid, such as off the ocean floor or surface, resulting in multi-path distortion. Depending on the relative levels of the signals and the noise, certain features which are present in the signals which are used to make the determination may be masked by the noise, or at least their detection may be difficult.
D. Ferkinhoff, et al., "Evidence Generation And Representation For Model Uncertainty Management In Nonlinear State Estimation," Proc. 25th Asilomar Conference On Circuits, Systems and Computers, November 1991, (incorporated by reference), describes a system which seeks to solve this problem by using a residual sequence generated that reflects difference values between said data sequence and an expected data sequence as would be represented by a selected model. The residual values and other information are used to generate a multitude of probability values representing the belief that pertinent identifiable features exist in the data sequence, and also values representing the belief that they do not exist in the data sequence, and further values representing the belief that the existence or non-existence of the features in the data sequence is not determinable. The belief is used to select an alternate model and the data sequence is processed again in response to that selected model. The process is repeated to verify the model selection.